Object category recognition has long been a central topic in computer vision research.
Traditionally, object recognition has been addressed by supervised learning using a large dataset of image-label pairs [1]}.
However, with supervised approaches, the model can only recognize a frozen set of object classes and is not suitable for real-world object recognition, where numerous object classes exist.
Recently, image recognition methods based on contrastive learning using image-text pair datasets have emerged [2]}, [3]}.
By training on hundreds of millions of image-text pairs, these models have acquired remarkable zero-shot recognition capabilities for a wide variety of objects.
However, these models can recognize objects that commonly appear in the pre-training dataset but are not as effective for rare objects [4]}.
Collecting new data and retraining the entire model to make these models recognize novel objects is impractical considering the cost of data collection and computation.
Therefore, it is essential to develop a method that enables the model to recognize novel objects while maintaining low data collection costs and avoiding model retraining as much as possible.
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